In-situ multi-phase flow imaging for particle dynamic tracking and characterization: Advances and applications

Liu, Jiaqiang; Kuang, Wenjie; Gao, Zhenguo; Rohani, Sohrab; Gong, Junbo

doi:10.1016/j.cej.2022.135554

Cited by 18 publications

(8 citation statements)

References 115 publications

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“…In this area, it is often assumed that the individual fluids are well separated, that is, the fluids do not mix and there are no additional particles resolved in the fluids (J. Liu et al, 2022;Zhang et al, 2022). Other applications of multiple fluids in contact with solid are found in industrial processes.…”

Section: Discussionmentioning

confidence: 99%

Partial Fourier Transform Methods to Solve the Solution Formula of Stokes Equation in Half-Space

Maryani

Zahratunnisa

Sihwaningrum

et al. 2022

j. sains. teknologi.

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Fluids are a shape of a matter which have substance liquids, gases and plasmas. In our daily life, fluids become important part, such as part of our blood and also help our body getting nutrients. It is well known that fluid motion can be described in mathematical model in especially in form of partial differential equations (PDE) and called as Navier Stokes Equations (NSE). The Navier Stokes equation is derived from balance of conservation of mass and conservation of momentum. In this paper, we consider the solution formula of the linearized of the Navier Stokes Equation (NSE) with the initial boundary value (IBV) problem in half space without surface tension. The model problem under consideration covers of non-linear fluid type. We solve the solution formula of velocity and density of the model problem by using Fourier transform and partial Fourier transform method. The strategy geting the solution of the model problem is based on the analysis of some resolvent of the model problem which obtained by using Laplace transform of the Stokes equations. Therefore, In particular, the formula of velocity and density of the Stokes equation are obtained.

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Section: Discussionmentioning

confidence: 99%

Partial Fourier Transform Methods to Solve the Solution Formula of Stokes Equation in Half-Space

Maryani

Zahratunnisa

Sihwaningrum

et al. 2022

j. sains. teknologi.

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“…Then, the transmitted light is received by a high magnification CCD camera to record the images of particles. The image acquisition is followed by image analysis with Pixact Emulsion Monitoring (PEM) system to detect dispersed phase elements, such as drops, bubbles, and crystals . As a result, detailed statistics for these elements, including number-based, area-based, and volume-based droplet size distribution are obtained as shown in Figure .…”

Section: Methodsmentioning

confidence: 99%

CFD-PBE Simulation of Flow Dynamics and Mass Transfer in Two-Stage Countercurrent Mixer-Settler

Zheng

Yang

Xiao

et al. 2023

Ind. Eng. Chem. Res.

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Flow condition and mass transfer behavior of liquid−liquid extraction in a two-stage countercurrent extractor are investigated experimentally and numerically. CFD-PBE simulation is adopted to analyze the impact of operating parameters, including impeller speed, residence time, and organic-aqueous ratio of feed flow on volume fraction and droplet size of dispersed phase and volumetric mass transfer coefficient. Droplet size distribution is calculated by considering breakage and coalescence of dispersed phase. The allocation of dispersed phase between stages is found to be determined by impeller speed. Meanwhile, the allocation of dispersed phase between mixers and settlers is governed by residence time and organic-aqueous ratio of feed flow. Turbulence intensity, volume fraction, and droplet size distribution are revealed to be dominating parameters that affect the spatial nonuniform distribution of volumetric mass transfer coefficient. The mass transfer rate can be improved by increasing impeller speed in two stages. Higher residence time and organic-aqueous ratio of feed flow, however, have opposite effects on mass transfer rate in stage-1 and stage-2.

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“…However, it is sensitive to background noise and has poor robustness. Other traditional image processing methods their characteristics and applications have been reviewed in another review . In brief, traditional methods have limitations when it comes to the automated handling of images and distinguishing crystals with varying morphologies.…”

Section: Introductionmentioning

confidence: 99%

“…Other traditional image processing methods their characteristics and applications have been reviewed in another review. 42 In brief, traditional methods have limitations when it comes to the automated handling of images and distinguishing crystals with varying morphologies. Moreover, these methods necessitate the use of high-quality images, requiring complex preprocessing, including adjusting contrast, manually separating polycrystals, etc.…”

Section: ■ Introductionmentioning

confidence: 99%

Artificial Intelligence Assisted Pharmaceutical Crystallization

Zhu,

Zhang,

Wang

et al. 2024

Crystal Growth & Design

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The ever-increasing demand for novel drug development has spurred the adaptation of conventional research methods in the era of artificial intelligence. Pharmaceutical crystallization, as an essential part of drug development, has become a thrilling research frontier that reduces screening labor via integrating automated high-throughput platforms with in situ monitoring and datadriven algorithms (e.g., machine learning) to predict physicochemical properties and various solid-state forms. In this review, we started with a primer to introduce the machine learning algorithms that are widely used in pharmaceutical crystallization. Then, we systematically summarized recent advancements on high-throughput platforms to acquire huge amounts of data sets, prediction of physicochemical properties based on abundant experimental data, optimization and monitoring of pharmaceutical crystallization process to screen crystallization conditions, and prediction of polymorphs and cocrystals. Finally, we discussed the challenges and opportunities in an endeavor to develop a fully automated pharmaceutical crystallization screening paradigm for ultimately realizing a selfdriving screening laboratory. This review highlights the frontier of artificial intelligence in pharmaceutical crystallization and offers a guideline for beginners to not only understand the basic principles of machine learning algorithms but also learn how to utilize machine learning to accelerate pharmaceutical crystallization development.

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In-situ multi-phase flow imaging for particle dynamic tracking and characterization: Advances and applications

Cited by 18 publications

References 115 publications

Partial Fourier Transform Methods to Solve the Solution Formula of Stokes Equation in Half-Space

Partial Fourier Transform Methods to Solve the Solution Formula of Stokes Equation in Half-Space

CFD-PBE Simulation of Flow Dynamics and Mass Transfer in Two-Stage Countercurrent Mixer-Settler

Artificial Intelligence Assisted Pharmaceutical Crystallization

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